Systems and Methods for Generating a Skull Surface for Computer Animation

ABSTRACT

An animation system wherein a machine learning model is adopted to generate animated facial actions based on parameters obtained from a live actor. Specifically, the anatomical structure such as a facial muscle topology and a skull surface topology that are specific to the live actor may be used. A skull surface that is specific to a live actor based on facial scans of the live actor and generic tissue depth data. For example, the facial scans of the live actor may provide a skin surface topology of the live actor, based on which the skull surface underneath the skin surface can be derived by “offsetting” the skin surface with corresponding soft tissue depth at different sampled points on the skin surface.

CROSS REFERENCES

The present disclosure is a nonprovisional of and claims priority tocommonly-owned and co-pending U.S. Provisional Applications 63/080,468,filed on Sep. 18, 2020, and 63/084,184, filed on Sep. 28, 2020.

This application is related to commonly-owned and co-pending U.S.Nonprovisional application Ser. No. ______ (attorney docket no.60152.62US01), filed on the same day.

All of the above mentioned applications are hereby expresslyincorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The present disclosure generally relates to tools for generatingcomputer-generated imagery. The disclosure relates more particularly toapparatus and techniques for generating muscle models usable byanimators in creating computer-generated imagery.

BACKGROUND

Many industries generate or use computer-generated imagery, such asimages or video sequences. The computer-generated imagery might includecomputer-animated characters that are based on live actors. For example,a feature film creator might want to generate a computer-animatedcharacter having facial expressions, movements, behaviors, etc. of alive actor, human or otherwise. It might be possible to have an animatorspecify, in detail, a surface of the live actor's body, but that can bedifficult when dealing with facial expressions and movements of the liveactor, as there are many variables.

Existing animation systems may recreate, in detail, a skin surface ofthe computer-animated character that closely resembles a live actor. Tosimulate the movements and/or facial expression of the computer-animatedcharacter that may appear to be similar to those of the live actor,muscle movements performed by the live actor is decomposed and analyzed,which often entails knowledge of an anatomical model of the physicalbody of the live actor. For example, the anatomical model may includethe shape, mass, curvature, and/or other parameters that describe thestructure of the muscle layer and the skull of the live actor.

The anatomical model can usually include a large number of variables.For example, there are more than 650 skeletal muscles in the human body,of which over 40 are controlled by seven nerves in a human face.Obtaining a geometric structure of the distribution of the large numberof facial muscles can be challenging, and the vector representation of ahigh dimension to describe the facial muscle geometry may largelyincrease the complexity of computer animation.

SUMMARY

Embodiments described herein provide a computer-implemented method forgenerating a facial model of a live actor. A plurality of facial scansof the live actor are obtained. Each facial scan includes a respectiveskin surface and respective sensing data indicative of facial musclestrains corresponding to the respective skin surface. A tissue depthdataset is obtained, including a plurality of tissues depthscorresponding to a plurality of tissue depth points on a human face.Each tissue depth indicates a distance from a corresponding tissue depthpoint on the human face to a skull surface underneath the human face. Athree-dimensional (“3D”) facial skin topology of the live actor isdetermined, from the plurality of facial scans. A skull surface topologyis generated, from the three-dimensional facial skin topology and thetissue depth dataset.

In one embodiment, the determination from the plurality of facial scans,the three-dimensional facial skin topology of the live actor comprises:determining, for a sampled point on the three-dimensional facial skintopology, a corresponding point on a skull surface based on a respectivetissue depth corresponding to the sampled point; and determining theskull surface topology by interpolating a plurality of determinedcorresponding points on the skull surface.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates an animation pipeline that might be used to renderanimated content showing animation of a character based on a machinelearning model that is trained from scans of a live actor, according toone embodiment described herein.

FIG. 2 illustrates an example neural network, in an embodiment.

FIG. 3 illustrates an example of a data structure that might represent amuscle model, in an embodiment.

FIG. 4 illustrates inputs and outputs of an animation creation system,in an embodiment.

FIG. 5 provides an example diagram illustrating building the anatomicalmodel of the specific live actor for the animation pipeline shown inFIG. 1, according to one embodiment described herein.

FIG. 6 provides an illustrative diagram showing a pseudo muscle topologythat approximates the muscle geometry model of the live actor, accordingto one embodiment described herein.

FIG. 7 provides an illustrative diagram showing reduction in the degreeof freedom of the muscle topology data structure, according to oneembodiment described herein.

FIG. 8 illustrates a mapping between muscles and pseudo-muscles.

FIG. 9 illustrates a mapping between muscles and pseudo-muscles wheremuscle strains are predetermined functions of a pseudo-muscle strain.

FIG. 10 provides an illustrative diagram showing the derivation of askull structure of the live actor based on facial scans and generictissue depth data, according to one embodiment described herein.

FIG. 11A provides a block diagram illustrating a process of generatingand refining the skull surface of the live actor, according to oneembodiment described herein.

FIG. 11B provides an example user interface diagram illustrating aneditor tool for editing the skull surface, according to one embodimentdescribed herein.

FIG. 12 illustrates an example visual content generation system as mightbe used to generate imagery in the form of still images and/or videosequences of images, according to various embodiments.

FIG. 13 is a block diagram illustrating an example computer system uponwhich computer systems of the systems illustrated in FIGS. 1-12 may beimplemented.

DETAILED DESCRIPTION

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Video applications nowadays may adopt computer-animated technology tocreate simulated characters, human or non-human, to appear a video. Forexample, the film industry has been using computer animation to generatecharacters that is often physically difficult or even impossible to beplayed by human actors. The physical appearance of suchcomputer-animated characters may be designed and controlled by ananimator, via configuring time-varying parameters to simulate themuscle, joint and bone structures and movements of a living creature,human or non-human. In this way, the computer-animated character may becreated to emulate the persona of a real living creature.

As used herein, an animator may refer to a human artist, filmmaker,photography image creator, or the like, who seeks to generate one ormore images (such as a video sequence forming an animation) based onanimator input and other data available to the animator. In someembodiments, the animator might be an automated or partially automatedprocess. Animator inputs might include specifications of values forpositions of movable elements. For example, an articulated character'smovement might be specified by values of each available joint in thecharacter.

As used herein, a rig may refer to a representation of data thatcorresponds to elements of a character, the allowed movements, etc. Onesuch rig is a facial rig. An animator might be provided with a userinterface of an animation creation system that allows the animator toinput values for various movable elements of the facial rig. Somemovable elements might be a jaw and a collection of muscles. From aspecification of provided variables of the movable elements of thefacial rig, the animation creation system can generate a pose of thefacial rig. For example, when variables corresponding to an amount ofcontraction for the muscles on either side of the mouth are set tovalues that represent maximum contraction of those muscles, theanimation creation system would output a pose with a face having awidened mouth. By varying the variables from frame to frame, and thuschanging poses from frame to frame, animation creation system can outputpositions of elements, thicknesses of elements, etc., which might beprovided as input to a rendering system.

A state of a facial rig corresponding to a particular expression,movement, or placement of elements of the facial rig so as to convey anexpression or positioning of facial elements might be represented incomputer memory as a data structure such as a strain vector. A strainvector might have components representing jaw position, eye positions,and strain values for each muscle in the facial rig that can bespecified by the strain vector. Thus, a particular expression of a liveactor can be represented by a strain vector and that strain vector canbe used to move or position elements of a facial rig—of that live actor,of a fanciful character, etc.—for generating computer-generated imagery.In some embodiments, the strain value components are one per muscle eachhaving a value representing a present strain value for its correspondingmuscle. A strain value might have a fixed value for a muscle in aneutral position for that muscle and a range of values coveringcontractions relative to the neutral position relaxations relative tothe neutral position. In a very specific embodiment, a neutral positionvalue for a strain is zero, a strain value for a muscle contractedrelative to the neutral position is a negative number, and a strainvalue for a muscle relaxed relative to the neutral position is apositive number. The strain value in that specific embodiment mightcorrespond to a length assigned to the muscle in the correspondingposition.

Given that a facial rig might comprise a large number of muscles,manually and individually setting each muscle's strain value in thestrain vector can be a tedious process and it can be hard to manuallymatch the strain vector component values to a desired state orexpression.

7In one embodiment, an animator can generate animation of a face of acharacter making an expression, perhaps talking according to certainspeech, and moving around by inputting, or otherwise specifying ordetermining, a set of strains, wherein a strain is a metric of a musclethat can be moved. In an example, a strain of a muscle is represented asa numerical value where 0.0 corresponds to the muscle in a rest ordefault position, a positive number corresponds to muscle contractionand a negative number corresponds to muscle relaxation. For example, thenumerical value for a strain, S_(M), of a muscle, M, might be asexpressed in Equation 1.

S _(M)=(rest_length(M)−length(M))/rest_length(M)  (Eqn. 1)

A difficulty with animating a face is that there are a large number offacial muscles and specifying a strain for each can be tedious,especially where many scenes need to be created. Another difficulty isin creating a model for the facial rig, specifying where each muscleattaches to a skull and to skin, or some other facial feature. For ananimated face to look right, it is often necessary that the strainscorrespond to actual strains in live actor muscles, but it is notpractical to determine, even for one live actor, where each muscleconnects on that actor's skull and skin, the thicknesses of the muscles,subdermal structures, etc. and thus the building of a model and a facialrig that correspond to a particular live actor. Yet another complexityis that there might be some expressions or animations that might berepresentable by a predetermined set of movements of a selected subsetof muscles and if there are a large number of muscles in that subset,moving them in concert might be tedious.

In an embodiment described herein, expressions of an actor aredetermined for example, by scanning the actor's face to capture scanneddata, which can identify how points on the surface of the actor's facemove in three dimensions. A number of expressions can be scanned. Whileit is generally known which muscles are present in a face of awell-studies species, such as humans, there can be variability in wherethe muscles are attached, which are activated, where both ends areattached, their thicknesses, and what range of strains are possible. Forexample, a person with a “jowly” face would have different values forthose than a person with a non-jowly face. As another example, there aresome actors who are able to strain muscles (i.e., move them away fromtheir rest positions) to cause their ears to wiggle and there are otherswho cannot strain those muscles. In some cases, the scanning orexpression determination might be done on actors who are no longerliving, perhaps from two-dimensional (“2D”) or three-dimensional (“3D”)recordings of the actor when alive.

An animation creation system and animators who use the animationcreation system might want a model for a specific live actor, includingtheir skull shape, muscle-to-skull attachment positions, musclethicknesses, etc., to construct a facial rig that can be animated, butthat might not be possible to determine directly from the live actor.One solution is to only rely on scanned expressions and the animator ispermitted to create other expressions not directly scanned by specifyinga linear combination of the available scans, but that is oftenconstraining. The shape blending system might be treated as a rig thatthe animator can control by specifying blend shape weights, but forfaces, this can be hard. Some results might be less than desirable as ahuman can create an almost infinite number of facial expressions and notall of those can be captured for blending.

In some embodiments, an AI system can generate a trained manifold basedon inputs from the scan results, dynamic muscle activations, and ananatomical model. A physics-based muscle simulator might be used togenerate dynamic muscle activations that are then consumed by the AI toproduce a trained manifold. The anatomical model, for example, mayinclude the muscle topology of the specific live actor, e.g., shape,length, position, distribution, mass, and/or the like, of the facialmuscles, and the skull surface of the specific live actor, e.g., theshape, dimension, curvature parameters, and/or the like of the skull.

To build the muscle topology of the specific live actor, some existingsystems may adopt live sensing data from the live actor—for instance, anumber of fiducial markers (e.g., see 104 in FIG. 1) may be placedaround the head area of the live actor to capture sensing data of themuscle position and movement. The distribution of the fiducial markersmay be determined in reference to an anatomy book, e.g., a genericfacial muscle distribution map. The capture sensing data relating to thefacial muscles may then be used to build a muscle topology that ismodeled as a polygon. The polygon facial muscle topology, however, canbe overly complicated for data processing. For instance, the human facecontains at least 43 muscles, and each of them may be described bymultiple surfaces and edges, which eventually results in vectorrepresentations of the muscle topology in a high-dimensional space andsignificant computational burden on the machine learning engine.

In view of the need for a muscle topology for efficient machinelearning, embodiments described herein generate a muscle structure ofsimplified “pseudo” muscles that approximate the actual muscle topologybut with reduced degree of freedom. Specifically, instead of directlyusing a muscle polygon topology generated from sensing datacorresponding to facial scans of the live actor, a subset of surfacesand/or edges of the muscle polygon topology are selected, aspseudo-muscles, to represent and approximate the muscle polygontopology. For example, each pseudo-muscle may appear in the form of amuscle curve, described by the tuple of curve start position, curve endposition, and curve length. Thus, the changes of the curve startposition, curve end position and/or the curve length may be used todenote the muscle strain value. As the number of muscle curves are muchsmaller than the variables required to describe a muscle polygontopology, the degree of freedom of the “pseudo” muscle topology can besignificantly reduced to alleviate the computational burden of themachine learning engine.

Thus, a muscle model including a pseudo-muscle that is represented bymuscle movements but not having a counterpart in a set of muscles, butinstead representing and mapping to movements of other muscles can beused in the animation system.

In addition, the skull structure of the live actor may also providevaluable information for the machine learning system to learn the musclemovement of the live actor and the resulting skin surface. This can bean improvement over using a generic human skull topology, e.g., relyingon the common anatomical bone structure of human skulls to build theskull topology, as the shape, size, curvature and/or other parameters ofthe human skull may vary person by person. The difference in skullstructures across different live actors, though subtle, can affect theaccuracy of simulated human facial expression. For example, live actor Awho has prominent cheekbones might have a different range of expressionsthan live actor B having less prominent cheekbones.

Alternatively, some other systems obtain specific skull surface for aspecific live actor based on invasive procedures, such as magneticresonance imaging (MRI) or X-ray scans. Such invasive methods can beboth expensive and inconvenient and can hardly be implemented at amassive scale when a large number of live actors are involved in filmproduction.

In view of the need for a convenient and accurate skull surface of thelive actor in computer animation, embodiments described herein mightgenerate a skull surface that is specific to a live actor based onfacial scans of the live actor and generic tissue depth data.Specifically, an animation system might process the facial scans of thelive actor to obtain a skin surface topology of the live actor, based onwhich the skull surface underneath the skin surface can be derived by“offsetting” the skin surface with corresponding soft tissue depth atdifferent sampled points on the skin surface.

In one implementation, the generic tissue depth data may be obtainedfrom existing datasets, and the tissue depth data may be grouped basedon a common characteristic, such as race, age, gender, body mass index,and/or the like. For example, when the live actor is a Caucasian male,generic tissue depth data may be selected from a dataset correspondingto Caucasian males.

As used herein, the term “topology” refers to properties of a geometricobject that are preserved under deformations, such as stretching,twisting, crumpling and bending. For example, a facial muscle topologyincludes a set of data depicting the positions, shapes, and strains offacial muscles.

As used herein, the term “surface” refers to geometric properties of theoutside layer of an object. For example, a skull surface includesparameters that describe the geometric properties of the outer layer ofthe skull, such as the curve, shape, and/or the like.

FIG. 1 illustrates an animation pipeline 100 that might be used torender animated content showing animation of a character based on amodel and a rig that is generated from scans of a live actor. Asillustrated there, a live actor 102 (“Actor A”) might be outfitted withfiducials 104 and have their face, expressions and/or body scanned by ascanner 106. The scanner 106 would then output or store results ofscanning to a scan results store 108. The fiducials 104 allow for thescan data that results from scanning to include indications of howspecific points on the surface of the face of the live actor 102 movegiven particular expressions. If the scanner 106 captures data in threedimensions (“3D”), the scan data could also indicate the surfacemanifold in 3D space that corresponds to the surface of the live actor'sface. While it might be expected that the skull of the live actor 102 isa constant shape and changes only by translations and rotations (and jawmovement), it is not expected that the surface manifold would beconstant, jaw movements, air pressure in the mouth, muscle movements,and other movable parts move and interact. Instead, different movementsand facial expressions result in different thicknesses, wrinkles, etc.of the actor's face.

The output from the scanner 106 may be stored as scan results 108, whichmay include a skin surface representation, muscle parameters, jointparameters, strain parameters, and/or the like.

In one implementation, the scan results 108 may also be used to generateone or more data bundles of scan results over a data bundle time periodT For example, each data bundle records a respective time-varying vectorrepresenting changes of the skin surface, muscle parameters, jointparameters, strain parameters, and/or the like over the data bundle timeperiod T Further details of the data bundles over a bundle time periodare discussed in commonly-owned and co-pending U.S. ProvisionalApplication Nos. 63/076,856 and 63/076,858, both filed on September 10,which are both hereby expressly incorporated by reference herein intheir entirety.

In one embodiment, it might be assumed that each human actor has more orless the same facial muscles, and more or less the same bone structurein the skull. An anatomical model dataset 112 might be provided thatrepresents muscles, where they connect, what other typical facialelements are present (eyes, eyelids, nose, lips, philtrum, etc.) andother features likely common to most human faces. The anatomical modeldataset 112 may also provide a generic skull structure dataset thatrepresents each piece of bone in the skull, the respective positionparameters, the respective shape parameters, the respective connectorparameter connecting to a neighboring piece, and/or the like. Of course,not all human faces and skulls are identical, and the actual positionsof muscles, their thicknesses, where they connect to, how much they canrelax and contract, are details that can vary from person to person, aswell as the shape of their skull. In this respective embodiment, theanatomical model 112 may represent a customized muscle and/or skulldataset that is specific to Actor A, or alternatively, a generic muscleand/or skull dataset which is pre-stored in the database of anatomicaldatabase.

In one embodiment, this anatomical model 112 can be provided to a musclesimulator 110 that performs physics-based muscle simulation and providesa dynamic muscle activations dataset 113 for Actor A.

In another embodiment, the anatomical model 112 may provide a genericmuscle model of muscle and skull that can be used as a reference point,together with the scan results 108 of the live actor A to provide, bythe pseudo-muscle generation module 114, a specific muscle topology thatis (1) customized to Actor A, and (2) has a simplified approximationstructure for machine learning. Further details of the pseudo-musclegeneration module 114 are further provided in relation to FIGS. 2-4.

In one embodiment, the scan results 108 can also be fed to a skullstructure generation module 115, which may in turn derive the skullstructure topology for Actor A from the live scan results 108 and datarelating to generic facial tissue mass. Further details of the skullstructure generation 115 are further provided in relation to FIGS. 2 and5-6.

In one embodiment, the generated skull structure topology from skullstructure generation 115, the pseudo-muscle topology from thepseudo-muscle generation module 114, together with the data bundlesrepresenting the time-varying vectors of muscle parameters, skinrepresentation, joint representation, and/or the like over a bundle timeperiod, may be input to the machine learning model 118. Based onparameters in the data bundles such as parameters of the muscles,strains, joints, and/or the like, skull parameters from the skulltopology, static muscle parameters from the pseudo-muscle topology, themachine learning model 118 generates a predicted skin surfacerepresentation (e.g., the visible facial expression such as “smile,”“frown,” etc.). In this way, the machine learning model 118 can learn atransformation between parameters of the muscles, strains, joints,and/or the like and the skin surface representation of actor A through atraining dataset in the form of data bundles representing scan results108 from the actor A. Further details of the training process formachine learning model 118 may be found in FIG. 2.

Thus, the machine learning system 118 adopts the derived skull structurefrom module 115 and a pseudo-muscle topology from module 114 that arespecific to Actor A, instead of generic human anatomical data. In thisway, the machine learning system 118 establishes a more accurate(compared to what was traditionally done using generic anatomical data)transformative relationship that is specific to Actor A, between ActorA's skin representation and the underlying muscle/joint movementparameters of Actor A. For instance, the underlying muscle/jointmovement parameters may include the positions of the muscles that aremoved, strain values, direction of movements, attachment points to thejoint, and/or the like, all of which are specific to Actor A.

In addition, as the pseudo-muscle topology generated from module 114 hasa reduced degree of freedom compared with a traditional facial musclepolygon topology, the computational efficiency of machine learning model118 may be improved.

In one implementation, the machine learning model 118 may be trained toinfer the shape of the live actor's skull, volume of muscles, range ofmotion, etc., to build a manifold of possible movements for the actor.The machine learning model 118 might output a manifold to be stored inmanifold storage 116. The manifold might represent the range ofplausible expressions. Logically, the manifold might represent a set ofsolutions or constraints in a high-dimension space corresponding to astrain vector.

For example, the machine learning model 118 may be trained to determinean action locus for the movement or expression adjustment (e.g., from“smile” to “grin”) and a range of action (e.g., widened month, showingof teeth, changed upward angle of the mouth, etc.) made by the actor A,based on the data bundles from the data bundle generation 114. Themachine learning model 118 may then determine a subset of the musclestrain vector applicable to the range of action, e.g., which muscles areused, and what are the corresponding strains. The machine learning model118 may determine the manifold that limits changes to the data bundle tochanges in the subset of the muscle strain vector. For example, for themovement or expression adjustment (e.g., from “smile” to “grin”), themanifold model 116 may limit the changes to the strain vectors in thedata bundle to a subset of muscle strain vectors relating to musclesthat widen the month and show teeth, and the corresponding strains thatchange the upward angle of the mouth.

Correspondingly, the manifold model 116 also limits the search ofupdated vector values for muscle vectors or strain vectors to a manifoldof allowed values for an updated cache of data vectors when the movementor expression adjustment (e.g., from “smile” to “grin”) takes place. Themanifold model 116 of allowed values correspond to known feasibleexpressions of the live actor.

Using an animation creation system 120, an animator 112 could generatemeshes that correspond to facial expressions of the live actor for whomthe muscle model was derived. A mesh might be stored in a meshdeformation store 124. If mesh corresponded to the facial surface ofActor A, the animation creation system 120 could be used by the animator122 to generate a facial surface of an expression that was notspecifically made by Actor A, but would be near what it would be ifActor A had tried that expression. The animation creation system 120might constrain an animator's inputs by projecting them onto themanifold, which would have an effect of transforming animator inputsthat are not corresponding to a plausible expression into a strainvector that does correspond to a plausible expression. The animator'sinputs might be represented in memory as a strain vector, havingcomponents corresponding to some facial muscles, as well as otheranimation variables that might not be related to muscles or that aremore easily represented directly, such as jaw movement, eye movement,and the like. A strain vector might be represented by an array ofvalues, wherein a value in the array corresponds to a vector componentthat is a value of strain in a particular dimension, representing strainon one muscle perhaps.

As for inputs and outputs of an animation creation system 120, inputsmight include an input strain vector, indicative a strain values forsome or all of the muscles in the muscle model, and values for the otheranimation variables, such as a scalar value for a jaw angle, two 2Dvectors corresponding to rotations of the eyes, etc. Along with themuscle model, which describes where the muscles are attached and theirvolume, and a skull model representing an actor's skull shape andcontour, and a control vector for other non-muscle animation variables,the animation creation system 120 can determine the volumes occupied bythe muscles, and thus the surface of the character's skin, and output amesh manifold of the character's skin, possibly depicting an expressioncorresponding to the input strain vector.

Using the above methods and apparatus, an animator can specify a facialexpression in the domain of muscle semantics, which can simplify ananimation process compared to limiting the animator to makingcombinations of recorded expressions as blends of the scanned facialshapes. In the general case, a length of a muscle is determined from itsstrain value and its rest length. Allowed strain values might beconstrained by the manifold so that strain values remain withinplausible boundaries. For a given scan of an expression on an actor'sface, a muscle model for that live actor, and a skull model for thatlive actor, an AI process can determine a likely strain vector that,when input to an animation generation system, would result in anexpression largely matching the scanned expression. Knowing the strainvalues, the animation generation system can provide those as the domainin which the animator would modify expressions. After training an AIsystem using dynamic scans of an actor's face as the ground truth fortraining, the muscle model can be derived that would allow for thesimulation of other expressions that were not captured.

In some instances, there might be more than one hundred musclesrepresented in the muscle model and the AI system that extracts a strainvector and a control vector from dynamic scans of the actor might beable to provide approximate solutions to match expressions. The controlvector might include other values besides jaw and eye positions.

As explained herein, an animation process might simulate facialexpressions through the use of a unique combination of hi-resolutionscans of a human face, simulated muscles, facial control vectors, andconstraints to generate unlimited facial expressions. In one embodiment,an AI system is employed to receive facial control vectors generatedfrom a series of muscle strain inputs and process those vectors relativeto a facial expression manifold configured to constrain facialexpressions of the simulation to plausible expressions. Simulation neednot be limited to simulating facial expressions that correspond to areal-world physical action, but more generally might be the generationof facial expressions informed by expressions made and recorded.

Separate AI systems might be used to train and derive the muscle modeland to train and derive the manifold. In some embodiments, in order tohit a target expression (and corresponding skin shape), the muscle modelmight be differentiable. An AI system might include a variationalauto-encoder (VAE).

The AI uses muscle control vectors, instead of blend shape weights orother approaches, and can then specify strains on those muscle controlvectors, which would in turn specify lengths of contractions of themuscles in a simulator. Each muscle scan be represented by a curve,which might have a length that is a function of the strain. A musclevector might comprise strains that affect a mesh representing the skinof a character. The muscles might include a rest length and attachmentpoint, and together represent a muscle geometry. Using the combinationof the input scans, the strains, the muscle control vectors, andmanifold constraints, an animation system can output plausible facialexpressions.

A renderer 126 can process the facial surface, perhaps mapping it to acharacter model from a character model store 128, such as a non-humancharacter played by Actor A, to form animated output that might bestored in animated output store 130.

FIG. 2 illustrates an example neural network system 200 in which scanresults are provided to a muscle simulator 202 and a neural network 204and an anatomical model to the muscle simulator 202. An output of themuscle simulator 202 is dynamic muscle activations for Actor A, which inturn are provided to neural network 204. Neural network 204 then outputsa manifold to manifold storage 208.

FIG. 3 illustrates an example of a data structure that might represent amuscle model. In that model, each muscle might be defined by a boneattachment point, a skin attachment point, and a muscle volume. Inanimation, as the strain on a muscle changes, the volume of the musclemight change shape, and the distance between the bone attachment pointand the skin attachment point might change, thus creating expressions.Additional elements might be included in a control vector that are forother animation variables.

FIG. 4 illustrates inputs and outputs of an animation creation system402. Inputs might include an input strain vector 404, indicative astrain values for some or all of the muscles in the muscle model, andvalues for the other animation variables, such as a scalar value for ajaw angle, two 2D vectors corresponding to rotations of the eyes, etc.Along with the muscle model, which describes where the muscles areattached and their volume, and a skull model representing an actor'sskull shape and contour, and a control vector for other non-muscleanimation variables, the animation creation system 402 can determine thevolumes occupied by the muscles, and thus the surface of the character'sskin, and output a mesh manifold of the character's skin, possiblydepicting an expression corresponding to the input strain vector 404.

FIG. 5 provides an example diagram illustrating building the anatomicalmodel of the specific live actor for the animation pipeline shown inFIG. 1, according to one embodiment described herein. The muscleparameter aggregation 505 and the muscle topology approximation 515 maybe a combined module, or separate modules, which may serve similarfunctions as the pseudo-muscle generation 114 in FIG. 1. The skullstructure generation module 550 may be similar to module 115 in FIG. 1.

A number of facial scans 501 a-n may be obtained, e.g., see 108 in FIG.1, and used for anatomical data building. Each facial scan may includean image of the skin surface of the live actor, e.g., skin surface data504, and a set of facial muscle sensing data 503 accompanying the skinsurface data 504.

In one embodiment, the muscle sensing data 503 from the facial scans 501a-n and reference data from a muscle anatomy database 519 may be sent tothe muscle parameter aggregation 505. For example, the reference datamay include information of a generic muscle topology of a human being,such as a start connection point of the muscle, an end connection pointof the muscle, additional attachment point (to the skull) of the muscle,the shape of the muscle, the curvature of the muscle, and/or the like.The muscle sensing data 503 collected by the fiducial markers 104 maytake a form, in one example, as (marker index, muscle name, musclestart, muscle end, marker position, strain value, . . . ). In this way,the muscle sensing data 503, together with the reference data, may beused to construct a muscle topology customized to the live actor.

The muscle topology generated by the muscle parameter aggregation 505might be represented as a geometric data structure, such as a shape of apolygon positioned in a three-dimensional (“3D”) space. Each surface oredge of the polygon can then represent a muscle tissue, and each node ofthe polygon can then represent a connector point of the muscle that isattached to the skull. For example, one surface of the polygon structuremay represent the muscle “frontal eminence,” with one edge of therespective edge representing the start of the muscle “eye pupil center,”and another edge of the respective edge representing the end of themuscle “anterior point of the forehead.” Other edges of the surface forthe muscle “frontal eminence” may be used to define the connectors thatconnect “frontal eminence” to another piece of muscle.

As previously described, the generated muscle polygon topology 510 maybe directly fed to the ML system 118 for predicting a skin surface givena set of muscle strain values and/or joint parameters. However, thepolygon topology 510 has a high degree of freedom, and may introduceheightened complexity to the ML system 118. The muscle topologyapproximation module 515 may build a pseudo-muscle topology based on thepolygon topology 510 by extracting a number of pseudo-muscles from thegeometry of the polygon 510.

Specifically, a subset of facial muscles from the muscle polygontopology 510 may be selected and pseudo-muscles are generated toapproximate the subset of facial muscles in positions such that eachpseudo-muscle may represent or substitute a facial muscle the topology510. For example, based on generic human anatomical knowledge, somefacial muscles may have “subunits,” e.g., a portion of a muscle may actindependently from another portion of the muscle depending on how it isinnervated by the nervous system. In this case, the “subunit” of themuscle can be selected. For another example, the directions of skinmovements of the live actor may be observed, and a pseudo-muscle may beselected or placed at an area where the skin at the same area may not bewell captured by an anatomical muscle.

In one implementation, each pseudo-muscle takes a form as a muscle curvedescribed by the start of the muscle curve, the end of the muscle curve,and the line or curve connecting the start and the end. The muscle curvemay be a line segment connecting the start and the end of the curve, ormay follow the curvature of the underneath skull surface. The length ofthe muscle curve can be computed based on the start position and thesend position of the muscle curve. Thus, the set of muscle curves mayform a graph structure, with each edge of the graph structurerepresenting a “simplified” pseudo-muscle model, and each node of thegraph structure representing the connector that one muscle connects toanother muscle, e.g., the connectivity information betweenpseudo-muscles.

The muscle topology approximation 515 may further extract strains fromthe pseudo-muscle topology. For example, the muscle topologyapproximation 515 may map the muscle curves in the pseudo-muscletopology to the fiducial markers 104, which may in turn obtainmeasurements of the curve start and the curve end positions, and thecurve lengths at different times. For example, a mapping between thesubset of facial muscles and the generated pseudo-muscles can bedetermined such that the start position and the end position of eachpseudo-muscle can be mapped to and from the 3D position of acorresponding facial muscle in the polygon topology 510. In this way,“pseudo-strains” corresponding to the pseudo-muscles can be computedbased on the position changes of muscle curves.

For instance, a facial scan of the live actor in a neutral facialaction, and a facial scan of the live actor in a non-neutral facialaction (e.g., “angry,” “laugh,” “grin,” etc.) may be used to derive thepositions of facial muscles, which can in turn be mapped to respectivestart and end positions of pseudo-muscles (muscle curves). In this way,the muscle curve length (associated with the specific non-neutral facialaction) and the muscle curve rest length (associated with the neutralfacial action) may be determined, e.g., based on the respective startpositions and end positions of the muscle curves.

For example, the respective pseudo-muscle strain can then be computed asillustrated by Equation 2.

Strain(curve_index)=(curve_length_rest (curve_index)−curve_length_motion(curve_index))/curve_length_rest(curve_index)  (Eqn. 2)

The computed pseudo-strain values may then be used for the ML system 118to learn the pseudo-strains for a certain facial expression.

In some implementations, in order to reduce the dimension and degree offreedom of the pseudo-muscle model from the generated polygon topology510, a subset of facial muscles that largely represent the movements offacial muscles in the polygon topology 510 may be selected. For example,in one implementation, a representative muscle tissue may be selectedfrom a small cluster of adjacent muscle tissues for generating apseudo-muscle representing the small cluster. For another example, eachpseudo-muscle may be an “average” (in terms of the coordinates of thestart position and the end position) of a small cluster of adjacentmuscle tissues.

The selection of the subset of facial muscles for generating thepseudo-muscle may be constantly revised, e.g., based on the performancefeedback from a machine learning system 118. For example, differentselections of subsets of facial muscles may generate different sets ofpseudo-muscles, and these different sets of pseudo-muscles may be inputto the machine learning system 118 to generate facial manifolds. Themachine learning system 118 may learn from a loss between the generatedfacial manifolds and ground truth labels, while the selection of thesubset of facial muscles for generating pseudo-muscle model may change.For example, the final loss from the designed system, that is, a meansquared distance between the predicted and the ground-truth mesh shoulddecrease when more muscles are selected to form the pseudo-muscles.

In another implementation, the performance feedback may include animatorfeedback regarding usability of the generated mesh based on thepseudo-muscles. The animator feedback may be combined with the finalloss to fine tune the selection of the subset of muscles.

Alternatively, instead of directly using the computed strain values forthe pseudo-muscle topology in computer animation, a mapping between theselected subset of muscles from the polygon topology and pseudo-musclesmay be established. The mapping represents effects on the pseudo-musclestrain value that substitutes for effects on muscle strain values ofmuscles of the subset of “actual” muscles. Thus, the computed strainvalues over a time period may be converted to muscle strain valuescorresponding to the “actual” muscles, using the mapping. The converted“actual” muscle strain values corresponding to the “actual” muscles inthe polygon topology are sent to the animation system for computeranimation of a computer-generated character.

In one embodiment, the skin surface data 504 together with the referencedata indicating facial tissue depth from the muscle anatomy database 519may be fed to the skull structure generation 550 to construct a skullstructure 555 specific to the live actor. For example, the muscleanatomy database 519 may provide generic facial tissue depth dataincluding an average tissue thickness of each facial muscle, e.g.,supraglabella=3.9 mm, glabella=4.9 mm, etc.

In one implementation, the skull structure generation 550 may choose aspecific tissue depth dataset from available datasets, based on avariety of factors, such as the race, ethnicity, age, body weight,gender, body mass indicator, and/or the like. For example, genericfacial soft tissue thickness data for Caucasians may be provided inGreef et al., Large-scale in-vivo Caucasian facial soft tissue thicknessdatabase for craniofacial reconstruction, Forensic Science International159S, S126-S146, 5006, which is hereby expressly incorporated byreference herein in its entirety.

In one implementation, the skull structure generation 550 may then usethe reference facial soft tissue thickness data and the skin surfacedata 504 to derive a skull structure. For example, for each fiducialmarker position 504 from the facial scans 510 a-n, the skull structuregeneration 550 may map the respective fiducial marker to a soft tissuefrom a tissue array in the soft tissue thickness dataset, and may thencompute the position of a corresponding spot on the skull where the softtissue is attached to by offsetting the position of the fiducial markerby the respective thickness of the soft tissue. In this way, the skullstructure may be reconstructed by interpolating all the computedpositions of spots on the skull.

In one implementation, the skull structure generation 550 may furtherassess the pseudo-muscle topology 515 from the topology approximation515 to further incorporate personalized anatomical data into skullstructure generation. For example, when mapping a respective fiducialmarker in the skin surface data 504 to a soft tissue from a tissue arrayin the soft tissue thickness dataset, the pseudo-muscle topology 515 maybe used to provide an estimate of the shape and position of the muscleunderneath the position of the respective fiducial marker on the skinsurface.

The generated pseudo-muscle topology 515 and the skull structure data555 may then be provided and stored as part of the anatomical model 112,which can then be fed to the ML system 118 as described in relation toFIG. 1

FIG. 6 provides an illustrative diagram showing a pseudo-muscle topologythat approximates the muscle geometry model of the live actor, accordingto one embodiment described herein. The muscle geometry 603 illustratesan example polygon topology 210 generated by the muscle parameteraggregation 205. For example, each facial muscle 604, may be representedby a surface of the polygon geometry. The complexity of the mathematicaldescription of a polygon having a large number of surfaces maysignificantly increase the computational burden to the system.

The pseudo-muscle topology 605 (similar to 515 in FIG. 5) illustrates a“simplified” graph structure where each pseudo-muscle 606 takes a formof a curve in the 3D space. Thus, the pseudo-muscle topology 605 can bemathematically represented by a set of curves, each having a start andan end. The reduction in computational complexity can be furtherillustrated in FIG. 7.

FIG. 7 provides an illustrative diagram showing reduction in the degreeof freedom of the muscle topology data structure, according to oneembodiment described herein. The original polygon muscle topology 303may be described by a number of variables with a large degree offreedom, e.g., each muscle is described by the 3D coordinates of the setof bone attachment points, the set of skin attachment points, and themuscle volume, as shown in FIG. 3. The total number N of muscle pointsmay be a large number as the muscle points are sampled from a polygonstructure.

The reduced pseudo-muscle topology 305, instead, may be described by aset of muscle curves represented by (curve_start, curve_end,curve_length). For example, the curve_start and curve_end may bedescribed by a set of 3D coordinates, and the curve length may bederived from curve_start and curve_end. The total number n of curves canbe much smaller than the total number N of muscle points in the polygontopology.

For example, the dimension of each muscle geometry may be the number ofvertices of a muscle chunk times 3. If the muscle geometry (e.g., apolygon) has 200 vertices, the dimension will become 200×3=600. Reduceddimensions by using pseudo-muscle strains may have the dimension definedby the number of muscle curves derived from a muscle chunk. If a musclegeometry has 3 curves (including pseudo-muscle curves), then thedimension will become 3.

FIG. 8 illustrates a mapping of strains of muscles in a muscle model topseudo-muscles in a modified muscle model. As illustrated there, thereis a selected set of muscles to be replaced or partially affected bypseudo-muscles. While it is possible to have the selected set by all ofthe muscles, often a suitable modified model need only affect somesmaller subset. In the example shown, a first pseudo-muscle, S_(P1),maps to three muscles from a muscle model so that an animator'smanipulation of a strain of S_(P1), can result in changes to threestrains in the strain vector. For a second pseudo-muscle, Spa, fourmuscles from the muscle model are mapped to it so that an animator'smanipulation of a strain of Spa can result in changes to four strains inthe strain vector. The mapping between pseudo-muscles and muscles can belinear or otherwise. In some instances, an animator can choose to modifya muscle as well as a pseudo-muscle that affects that muscle. As thestrain value of the pseudo-muscle is adjusted, the strains of thecorresponding muscles are adjusted correspondingly.

FIG. 6 illustrates the case where the strains of the original musclesare not independently controlled, but are functions of the strain of apseudo-muscle. An animation process, such as a simulator or a rigmanipulator, might process a strain vector to determine a shape orexpression for a facial rig according to a provided strain vector, butin this case, the animator does not independently specify strains formuscles M₁, M₂, M₃, and M₄. Instead, their strains are computed by theanimation process as functions of the strain of a pseudo-muscle SP₁, andother strains might be independently specified.

A very specific example of the use of pseudo-muscles might occur wherean animator's user interface provides a display of a facial rig andallows for an animator to input strain values for the many radialmuscles attached to the lips of the facial rig. Rather than require thatthe animator specify each muscle's strain separately, the animator canspecify the strain on a pseudo-muscle that runs tangentially to themouth of the facial rig.

In some implementations the mapping to muscles need not be unique, inthat more than one pseduomuscle might map to a given muscle.

An AI system might determine the attachment points, volume, range ofstrain, etc. for the pseudo-muscle, based on training on models and/ortraining on prior animator inputs. An AI system might start with ananatomical default model and model facial muscles as geometry, such aspolygonal structures, as well as extracting muscle curves for lines ofaction and endpoints for the muscles. The AI system can then use machinelearning to (1) determine what selected set of muscles might be groupedtogether and replaced in whole or part by a pseudo-muscle, and (2)determine the lines of action and endpoints for the pseudo-muscle, aswell as determining what a suitable mapping might be between the strainsof the selected set and the pseudo-muscle.

Using the above methods and apparatus, an animator can specify a facialexpression in the domain of muscle semantics, which can simplify ananimation process compared to limiting the animator to makingcombinations of recorded expressions as blends of the scanned facialshapes. In the general case, a length of a muscle is determined from itsstrain value and its rest length. Allowed strain values might beconstrained by the manifold so that strain values remain withinplausible boundaries. For a given scan of an expression on an actor'sface, a muscle model for that live actor, and a skull model for thatlive actor, an AI process can determine a likely strain vector that,when input to an animation generation system, would result in anexpression largely matching the scanned expression. Knowing the strainvalues, the animation generation system can provide those as the domainin which the animator would modify expressions. After training an AIsystem using dynamic scans of an actor's face as the ground truth fortraining, the muscle model can be derived that would allow for thesimulation of other expressions that were not captured.

In some instances, there might be more than one hundred musclesrepresented in the muscle model and the AI system that extracts a strainvector and a control vector from dynamic scans of the actor might beable to provide approximate solutions to match expressions. The controlvector might include other values besides jaw and eye positions.

As explained herein, an animation process might simulate facialexpressions through the use of a unique combination of hi-resolutionscans of a human face, simulated muscles, facial control vectors, andconstraints to generate unlimited facial expressions. In one embodiment,an AI system is employed to receive facial control vectors generatedfrom a series of muscle strain inputs and process those vectors relativeto a facial expression manifold configured to constrain facialexpressions of the simulation to plausible expressions. Simulation neednot be limited to simulating facial expressions that correspond to areal-world physical action, but more generally might be the generationof facial expressions informed by expressions made and recorded.

Separate AI systems might be used to train and derive the muscle modeland to train and derive the manifold. In some embodiments, in order tohit a target expression (and corresponding skin shape), the muscle modelmight be differentiable. An AI system might include a variationalauto-encoder (VAE).

The AI uses muscle control vectors, instead of blend shape weights orother approaches, and can then specify strains on those muscle controlvectors, which would in turn specify lengths of contractions of themuscles in a simulator. Each muscle scan be represented by a curve,which might have a length that is a function of the strain. A musclevector might comprise strains that affect a mesh representing the skinof a character. The muscles might include a rest length and attachmentpoint, and together represent a muscle geometry. Using the combinationof the input scans, the strains, the muscle control vectors, andmanifold constraints, an animation system can output plausible facialexpressions. According to one embodiment, the techniques describedherein are implemented by one or generalized computing systemsprogrammed to perform the techniques pursuant to program instructions infirmware, memory, other storage, or a combination. Special-purposecomputing devices may be used, such as desktop computer systems,portable computer systems, handheld devices, networking devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques.

An AI system can be used to determine suitable pseudo-muscles and theirmapping to other muscles.

FIG. 10 provides an illustrative diagram showing the derivation of askull structure of the live actor based on facial scans and generictissue depth data, according to one embodiment described herein. Afacial scan 1001 (e.g., similar to facial scans 501 a-n in FIG. 5) mayprovide the physical appearance of the skin surface of a live actor. Atissue depth array 502, e.g., obtained from the muscle anatomy database519, may be applied to the facial scan 1001.

For example, the tissue depth array 1002 may include a plurality ofmuscle points (fiducial markers) and the corresponding average tissuethickness underneath the fiducial markers. A plurality of boneattachment points on the skull may then be derived based on the fiducialmarkers on skin surface and the average tissue thickness at 1003. Theplurality of bone attachment points may be used to construct, e.g., viainterpolation, a 3D skull surface 1005.

FIG. 11A provides a block diagram illustrating a process of generatingand refining the skull surface of the live actor, according to oneembodiment described herein. Facial scan 1001 that includes the skintopology and generic tissue dataset 1002 including a tissue depth arraymay be input to a machine learning engine 1010, which learns theunderneath skull structure corresponding to the facial scan 1001. In oneimplementation, the generic tissue dataset 1002 may be selected based onspecific race, ethnicity, age, or the body mass index of the live actor.

The machine learning engine 1010 may generate a skull surface 1012,which is further output to a model editor 1015. For example, the modeleditor 1015 may display the constructed 3D skull surface on a userinterface 1016, e.g., similar to 1005 in FIG. 5. A user, such as ananimator, etc., may edit the generated skull surface by submitting aninput 1018 via the user interface, e.g., by shifting, modifying,smoothing, adjusting, and/or the like certain parts of the generatedskull surface 1012. For example, the user may directly interact with agraphic user interface (GUI) to modify the skull topology, such asenlarging an eye socket, lowering the curve of cheekbone, and/or thelike. For another example, the user may enter specific parameters tochange the size, shape, and/or position coordinates of a specific bonein the skull.

In another implementation, the model editor 1015 may receivepersonalized facial parameters such as muscle position, muscle depth,joint parameter, and/or the like from a facial model dataset 1018. Thefacial model dataset 1018 may be a generic anatomical facial muscledataset, or a personalized facial muscle topology such as the musclegeometry 303 or the simplified pseudo muscle topology 305, both specificto the live actor. The model editor 1015 may further adjust the skullsurface 1012 based on the personalized facial muscle parameters. Forexample, the model editor 1015 may adjust the position of correspondingbone attachment points in the skull surface based on the position of acorresponding muscle (e.g., supraglabella, etc.) that is attached to therespective bone.

For example, FIG. 11B provides an example UI interface of the modeleditor 1015. The user may modify the skull that has been deformedaccording to the tissue depths by editing various parameters through themodel editor 1015. For instance, the user may manually move a ‘live’cage (not shown in FIG. 11B) around the generated skull surface thatallows some coarse deformation by moving vertices and affecting thehigher resolution skull contained within it. The modified deformationmay result in changed skull surface geometry. The user may furthermodify the skull surface geometry by changing the initial rigidalignment of the template skull inside of the actor skin, executing thedeformation, editing of tissue depth, modifying parameters such asethnicity, age, gender, bmi, tissue depth variation (min/max/mean),non-human scale (if required), toggling of visualisation aids, and/orthe like. The user may further apply coarse deformation by movingvertices on a ‘live’ cage around the skull as described above, andresult in final model refinement using any modelling tools available,resulting in moved vertices on the final skull mesh.

The model editor 1015 may thus generate a refined skull surface 1020,which may be used by the machine learning system 118 to learn therelationship between muscle parameters and skin surface representation.

The visual content generation system 1200 of FIG. 12 can be isconfigured to generate and process muscle models, facial rigs, andanimator user interfaces described in relation to FIGS. 1-11 and may beimplemented by software executing on one or more computer systems (e.g.,each like a computer system 1300 illustrated in FIG. 13).

For example, FIG. 12 illustrates the example visual content generationsystem 1200 as might be used to generate imagery in the form of stillimages and/or video sequences of images. The visual content generationsystem 1200 might generate imagery of live action scenes, computergenerated scenes, or a combination thereof. In a practical system, usersare provided with tools that allow them to specify, at high levels andlow levels where necessary, what is to go into that imagery. Forexample, a user might be an animation artist and might use the visualcontent generation system 1200 to capture interaction between two humanactors performing live on a sound stage and replace one of the humanactors with a computer-generated anthropomorphic non-human being thatbehaves in ways that mimic the replaced human actor's movements andmannerisms, and then add in a third computer-generated character andbackground scene elements that are computer-generated, all in order totell a desired story or generate desired imagery.

Still images that are output by the visual content generation system1200 might be represented in computer memory as pixel arrays, such as atwo-dimensional array of pixel color values, each associated with apixel having a position in a two-dimensional image array. Pixel colorvalues might be represented by three or more (or fewer) color values perpixel, such as a red value, a green value, and a blue value (e.g., inRGB format). Dimensions of such a two-dimensional array of pixel colorvalues might correspond to a preferred and/or standard display scheme,such as 1920 pixel columns by 1280 pixel rows. Images might or might notbe stored in a compressed format, but either way, a desired image may berepresented as a two-dimensional array of pixel color values. In anothervariation, images are represented by a pair of stereo images forthree-dimensional presentations and in other variations, some or all ofan image output might represent three-dimensional imagery instead ofjust two-dimensional views.

A stored video sequence might include a plurality of images such as thestill images described above, but where each image of the plurality ofimages has a place in a timing sequence and the stored video sequence isarranged so that when each image is displayed in order, at a timeindicated by the timing sequence, the display presents what appears tobe moving and/or changing imagery. In one representation, each image ofthe plurality of images is a video frame having a specified frame numberthat corresponds to an amount of time that would elapse from when avideo sequence begins playing until that specified frame is displayed. Aframe rate might be used to describe how many frames of the stored videosequence are displayed per unit time. Example video sequences mightinclude 24 frames per second (24 FPS), 50 FPS, 140 FPS, or other framerates. In some embodiments, frames are interlaced or otherwise presentedfor display, but for the purpose of clarity of description, in someexamples, it is assumed that a video frame has one specified displaytime and it should be understood that other variations are possible.

One method of creating a video sequence is to simply use a video camerato record a live action scene, i.e., events that physically occur andcan be recorded by a video camera. The events being recorded can beevents to be interpreted as viewed (such as seeing two human actors talkto each other) and/or can include events to be interpreted differentlydue to clever camera operations (such as moving actors about a stage tomake one appear larger than the other despite the actors actually beingof similar build, or using miniature objects with other miniatureobjects so as to be interpreted as a scene containing life-sizedobjects).

Creating video sequences for story-telling or other purposes often callsfor scenes that cannot be created with live actors, such as a talkingtree, an anthropomorphic object, space battles, and the like. Such videosequences might be generated computationally rather than capturing lightfrom live scenes. In some instances, an entirety of a video sequencemight be generated computationally, as in the case of acomputer-animated feature film. In some video sequences, it is desirableto have some computer-generated imagery and some live action, perhapswith some careful merging of the two.

While computer-generated imagery might be creatable by manuallyspecifying each color value for each pixel in each frame, this is likelytoo tedious to be practical. As a result, a creator uses various toolsto specify the imagery at a higher level. As an example, an artist mightspecify the positions in a scene space, such as a three-dimensionalcoordinate system, of objects and/or lighting, as well as a cameraviewpoint, and a camera view plane. From that, a rendering engine couldtake all of those as inputs, and compute each of the pixel color valuesin each of the frames. In another example, an artist specifies positionand movement of an articulated object having some specified texturerather than specifying the color of each pixel representing thatarticulated object in each frame.

In a specific example, a rendering engine performs ray tracing wherein apixel color value is determined by computing which objects lie along aray traced in the scene space from the camera viewpoint through a pointor portion of the camera view plane that corresponds to that pixel. Forexample, a camera view plane might be represented as a rectangle havinga position in the scene space that is divided into a grid correspondingto the pixels of the ultimate image to be generated, and if a raydefined by the camera viewpoint in the scene space and a given pixel inthat grid first intersects a solid, opaque, blue object, that givenpixel is assigned the color blue. Of course, for moderncomputer-generated imagery, determining pixel colors—and therebygenerating imagery—can be more complicated, as there are lightingissues, reflections, interpolations, and other considerations.

As illustrated in FIG. 12, a live action capture system 1202 captures alive scene that plays out on a stage 1204. The live action capturesystem 1202 is described herein in greater detail, but might includecomputer processing capabilities, image processing capabilities, one ormore processors, program code storage for storing program instructionsexecutable by the one or more processors, as well as user input devicesand user output devices, not all of which are shown.

In a specific live action capture system, cameras 1206(1) and 1206(2)capture the scene, while in some systems, there might be other sensor(s)1208 that capture information from the live scene (e.g., infraredcameras, infrared sensors, motion capture (“mo-cap”) detectors, etc.).On the stage 1204, there might be human actors, animal actors, inanimateobjects, background objects, and possibly an object such as a greenscreen 1210 that is designed to be captured in a live scene recording insuch a way that it is easily overlaid with computer-generated imagery.The stage 1204 might also contain objects that serve as fiducials, suchas fiducials 1212(1)-(3), that might be used post-capture to determinewhere an object was during capture. A live action scene might beilluminated by one or more lights, such as an overhead light 1214.

During or following the capture of a live action scene, the live actioncapture system 1202 might output live action footage to a live actionfootage storage 1220. A live action processing system 1222 might processlive action footage to generate data about that live action footage andstore that data into a live action metadata storage 1224. The liveaction processing system 1222 might include computer processingcapabilities, image processing capabilities, one or more processors,program code storage for storing program instructions executable by theone or more processors, as well as user input devices and user outputdevices, not all of which are shown. The live action processing system1222 might process live action footage to determine boundaries ofobjects in a frame or multiple frames, determine locations of objects ina live action scene, where a camera was relative to some action,distances between moving objects and fiducials, etc. Where elements aresensored or detected, the metadata might include location, color, andintensity of the overhead light 1214, as that might be useful inpost-processing to match computer-generated lighting on objects that arecomputer-generated and overlaid on the live action footage. The liveaction processing system 1222 might operate autonomously, perhaps basedon predetermined program instructions, to generate and output the liveaction metadata upon receiving and inputting the live action footage.The live action footage can be camera-captured data as well as data fromother sensors.

An animation creation system 1230 is another part of the visual contentgeneration system 1200. The animation creation system 1230 might includecomputer processing capabilities, image processing capabilities, one ormore processors, program code storage for storing program instructionsexecutable by the one or more processors, as well as user input devicesand user output devices, not all of which are shown. The animationcreation system 1230 might be used by animation artists, managers, andothers to specify details, perhaps programmatically and/orinteractively, of imagery to be generated. From user input and data froma database or other data source, indicated as a data store 1232, theanimation creation system 1230 might generate and output datarepresenting objects (e.g., a horse, a human, a ball, a teapot, a cloud,a light source, a texture, etc.) to an object storage 1234, generate andoutput data representing a scene into a scene description storage 1236,and/or generate and output data representing animation sequences to ananimation sequence storage 1238.

Scene data might indicate locations of objects and other visualelements, values of their parameters, lighting, camera location, cameraview plane, and other details that a rendering engine 1250 might use torender CGI imagery. For example, scene data might include the locationsof several articulated characters, background objects, lighting, etc.specified in a two-dimensional space, three-dimensional space, or otherdimensional space (such as a 2.5-dimensional space, three-quarterdimensions, pseudo-3D spaces, etc.) along with locations of a cameraviewpoint and view place from which to render imagery. For example,scene data might indicate that there is to be a red, fuzzy, talking dogin the right half of a video and a stationary tree in the left half ofthe video, all illuminated by a bright point light source that is aboveand behind the camera viewpoint. In some cases, the camera viewpoint isnot explicit, but can be determined from a viewing frustum. In the caseof imagery that is to be rendered to a rectangular view, the frustumwould be a truncated pyramid. Other shapes for a rendered view arepossible and the camera view plane could be different for differentshapes.

The animation creation system 1230 might be interactive, allowing a userto read in animation sequences, scene descriptions, object details, etc.and edit those, possibly returning them to storage to update or replaceexisting data. As an example, an operator might read in objects fromobject storage into a baking processor that would transform thoseobjects into simpler forms and return those to the object storage 1234as new or different objects. For example, an operator might read in anobject that has dozens of specified parameters (movable joints, coloroptions, textures, etc.), select some values for those parameters andthen save a baked object that is a simplified object with now fixedvalues for those parameters.

Rather than have to specify each detail of a scene, data from the datastore 1232 might be used to drive object presentation. For example, ifan artist is creating an animation of a spaceship passing over thesurface of the Earth, instead of manually drawing or specifying acoastline, the artist might specify that the animation creation system1230 is to read data from the data store 1232 in a file containingcoordinates of Earth coastlines and generate background elements of ascene using that coastline data.

Animation sequence data might be in the form of time series of data forcontrol points of an object that has attributes that are controllable.For example, an object might be a humanoid character with limbs andjoints that are movable in manners similar to typical human movements.An artist can specify an animation sequence at a high level, such as“the left hand moves from location (X1, Y1, Z1) to (X2, Y2, Z2) overtime T1 to T2”, at a lower level (e.g., “move the elbow joint 2.5degrees per frame”) or even at a very high level (e.g., “character Ashould move, consistent with the laws of physics that are given for thisscene, from point P1 to point P2 along a specified path”).

Animation sequences in an animated scene might be specified by whathappens in a live action scene. An animation driver generator 1244 mightread in live action metadata, such as data representing movements andpositions of body parts of a live actor during a live action scene, andgenerate corresponding animation parameters to be stored in theanimation sequence storage 1238 for use in animating a CGI object. Thiscan be useful where a live action scene of a human actor is capturedwhile wearing mo-cap fiducials (e.g., high-contrast markers outsideactor clothing, high-visibility paint on actor skin, face, etc.) and themovement of those fiducials is determined by the live action processingsystem 1222. The animation driver generator 1244 might convert thatmovement data into specifications of how joints of an articulated CGIcharacter are to move over time.

A rendering engine 1250 can read in animation sequences, scenedescriptions, and object details, as well as rendering engine controlinputs, such as a resolution selection and a set of renderingparameters. Resolution selection might be useful for an operator tocontrol a trade-off between speed of rendering and clarity of detail, asspeed might be more important than clarity for a movie maker to test aparticular interaction or direction, while clarity might be moreimportant that speed for a movie maker to generate data that will beused for final prints of feature films to be distributed. The renderingengine 1250 might include computer processing capabilities, imageprocessing capabilities, one or more processors, program code storagefor storing program instructions executable by the one or moreprocessors, as well as user input devices and user output devices, notall of which are shown.

The visual content generation system 1200 can also include a mergingsystem 1260 that merges live footage with animated content. The livefootage might be obtained and input by reading from the live actionfootage storage 1220 to obtain live action footage, by reading from thelive action metadata storage 1224 to obtain details such as presumedsegmentation in captured images segmenting objects in a live actionscene from their background (perhaps aided by the fact that the greenscreen 1210 was part of the live action scene), and by obtaining CGIimagery from the rendering engine 1250.

A merging system 1260 might also read data from a rulesets formerging/combining storage 1262. A very simple example of a rule in aruleset might be “obtain a full image including a two-dimensional pixelarray from live footage, obtain a full image including a two-dimensionalpixel array from the rendering engine 1250, and output an image whereeach pixel is a corresponding pixel from the rendering engine 1250 whenthe corresponding pixel in the live footage is a specific color ofgreen, otherwise output a pixel value from the corresponding pixel inthe live footage.”

The merging system 1260 might include computer processing capabilities,image processing capabilities, one or more processors, program codestorage for storing program instructions executable by the one or moreprocessors, as well as user input devices and user output devices, notall of which are shown. The merging system 1260 might operateautonomously, following programming instructions, or might have a userinterface or programmatic interface over which an operator can control amerging process. In some embodiments, an operator can specify parametervalues to use in a merging process and/or might specify specific tweaksto be made to an output of the merging system 1260, such as modifyingboundaries of segmented objects, inserting blurs to smooth outimperfections, or adding other effects. Based on its inputs, the mergingsystem 1260 can output an image to be stored in a static image storage1270 and/or a sequence of images in the form of video to be stored in ananimated/combined video storage 1272.

Thus, as described, the visual content generation system 1200 can beused to generate video that combines live action with computer-generatedanimation using various components and tools, some of which aredescribed in more detail herein. While the visual content generationsystem 1200 might be useful for such combinations, with suitablesettings, it can be used for outputting entirely live action footage orentirely CGI sequences. The code may also be provided and/or carried bya transitory computer readable medium, e.g., a transmission medium suchas in the form of a signal transmitted over a network.

According to one embodiment, the techniques described herein areimplemented by one or generalized computing systems programmed toperform the techniques pursuant to program instructions in firmware,memory, other storage, or a combination. Special-purpose computingdevices may be used, such as desktop computer systems, portable computersystems, handheld devices, networking devices or any other device thatincorporates hard-wired and/or program logic to implement thetechniques.

For example, FIG. 13 is a block diagram that illustrates a computersystem 1300 upon which the computer systems described herein and/or thevisual content generation system 1200 (see FIG. 12) may be implemented.The computer system 1300 includes a bus 1302 or other communicationmechanism for communicating information, and a processor 1304 coupledwith the bus 1302 for processing information. The processor 1304 may be,for example, a general purpose microprocessor.

The computer system 1300 also includes a main memory 1306, such as arandom access memory (RAM) or other dynamic storage device, coupled tothe bus 1302 for storing information and instructions to be executed bythe processor 1304. The main memory 1306 may also be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by the processor 1304. Such instructions,when stored in non-transitory storage media accessible to the processor1304, render the computer system 1300 into a special-purpose machinethat is customized to perform the operations specified in theinstructions.

The computer system 1300 further includes a read only memory (ROM) 1308or other static storage device coupled to the bus 1302 for storingstatic information and instructions for the processor 1304. A storagedevice 1310, such as a magnetic disk or optical disk, is provided andcoupled to the bus 1302 for storing information and instructions.

The computer system 1300 may be coupled via the bus 1302 to a display1312, such as a computer monitor, for displaying information to acomputer user. An input device 1314, including alphanumeric and otherkeys, is coupled to the bus 1302 for communicating information andcommand selections to the processor 1304. Another type of user inputdevice is a cursor control 1316, such as a mouse, a trackball, or cursordirection keys for communicating direction information and commandselections to the processor 1304 and for controlling cursor movement onthe display 1312. This input device typically has two degrees of freedomin two axes, a first axis (e.g., x) and a second axis (e.g., y), thatallows the device to specify positions in a plane.

The computer system 1300 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computer systemcauses or programs the computer system 1300 to be a special-purposemachine. According to one embodiment, the techniques herein areperformed by the computer system 1300 in response to the processor 1304executing one or more sequences of one or more instructions contained inthe main memory 1306. Such instructions may be read into the main memory1306 from another storage medium, such as the storage device 1310.Execution of the sequences of instructions contained in the main memory1306 causes the processor 1304 to perform the process steps describedherein. In alternative embodiments, hard-wired circuitry may be used inplace of or in combination with software instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperation in a specific fashion. Such storage media may includenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as the storage device 1310.Volatile media includes dynamic memory, such as the main memory 1306.Common forms of storage media include, for example, a floppy disk, aflexible disk, hard disk, solid state drive, magnetic tape, or any othermagnetic data storage medium, a CD-ROM, any other optical data storagemedium, any physical medium with patterns of holes, a RAM, a PROM, anEPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire, and fiber optics, including thewires that include the bus 1302. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to the processor 1304 for execution. Forexample, the instructions may initially be carried on a magnetic disk orsolid state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over anetwork connection. A modem or network interface local to the computersystem 1300 can receive the data. The bus 1302 carries the data to themain memory 1306, from which the processor 1304 retrieves and executesthe instructions. The instructions received by the main memory 1306 mayoptionally be stored on the storage device 1310 either before or afterexecution by the processor 1304.

The computer system 1300 also includes a communication interface 1318coupled to the bus 1302. The communication interface 1318 provides atwo-way data communication coupling to a network link 1320 that isconnected to a local network 1322. For example, the communicationinterface 1318 may be an integrated services digital network (ISDN)card, cable modem, satellite modem, or a modem to provide a datacommunication connection to a corresponding type of telephone line.Wireless links may also be implemented. In any such implementation, thecommunication interface 1318 sends and receives electrical,electromagnetic, or optical signals that carry digital data streamsrepresenting various types of information.

The network link 1320 typically provides data communication through oneor more networks to other data devices. For example, the network link1320 may provide a connection through the local network 1322 to a hostcomputer 1324 or to data equipment operated by an Internet ServiceProvider (ISP) 1326. The ISP 1326 in turn provides data communicationservices through the world wide packet data communication network nowcommonly referred to as the “Internet” 1328. The local network 1322 andInternet 1328 both use electrical, electromagnetic, or optical signalsthat carry digital data streams. The signals through the variousnetworks and the signals on the network link 1320 and through thecommunication interface 1318, which carry the digital data to and fromthe computer system 1300, are example forms of transmission media.

The computer system 1300 can send messages and receive data, includingprogram code, through the network(s), the network link 1320, andcommunication interface 1318. In the Internet example, a server 1330might transmit a requested code for an application program through theInternet 1328, ISP 1326, local network 1322, and communication interface1318. The received code may be executed by the processor 1304 as it isreceived, and/or stored in the storage device 1310, or othernon-volatile storage for later execution.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. Processes described herein (or variationsand/or combinations thereof) may be performed under the control of oneor more computer systems configured with executable instructions and maybe implemented as code (e.g., executable instructions, one or morecomputer programs or one or more applications) executing collectively onone or more processors, by hardware or combinations thereof. The codemay be stored on a computer-readable storage medium, for example, in theform of a computer program comprising a plurality of instructionsexecutable by one or more processors. The computer-readable storagemedium may be non-transitory. The code may also be provided carried by atransitory computer readable medium e.g., a transmission medium such asin the form of a signal transmitted over a network.

Conjunctive language, such as phrases of the form “at least one of A, B,and C,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with the context as used in general to present that an item,term, etc., may be either A or B or C, or any nonempty subset of the setof A and B and C. For instance, in the illustrative example of a sethaving three members, the conjunctive phrases “at least one of A, B, andC” and “at least one of A, B and C” refer to any of the following sets:{A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of A, at least one of B and at least one of C eachto be present.

The use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate embodiments ofthe invention and does not pose a limitation on the scope of theinvention unless otherwise claimed. No language in the specificationshould be construed as indicating any non-claimed element as essentialto the practice of the invention.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

Further embodiments can be envisioned to one of ordinary skill in theart after reading this disclosure. In other embodiments, combinations orsub-combinations of the above-disclosed invention can be advantageouslymade. The example arrangements of components are shown for purposes ofillustration and it should be understood that combinations, additions,re-arrangements, and the like are contemplated in alternativeembodiments of the present invention. Thus, while the invention has beendescribed with respect to exemplary embodiments, one skilled in the artwill recognize that numerous modifications are possible.

For example, the processes described herein may be implemented usinghardware components, software components, and/or any combinationthereof. The specification and drawings are, accordingly, to be regardedin an illustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims and that the invention is intended to cover allmodifications and equivalents within the scope of the following claims.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

1. A computer-implemented method for generating a skull surface of alive actor, the method comprising: obtaining a plurality of facial scansof the live actor, each facial scan including a respective skin surfaceand respective sensing data indicative of facial muscle strainscorresponding to the respective skin surface; obtaining, from adatabase, a tissue depth dataset including a plurality of tissues depthscorresponding to a plurality of tissue depth points on a human face,wherein each tissue depth indicates a distance from a correspondingtissue depth point on the human face to a skull surface underneath thehuman face; determining, from the plurality of facial scans, athree-dimensional facial skin topology of the live actor; generating,from the three-dimensional facial skin topology and the tissue depthdataset, a skull surface topology; and refining the skull surfacetopology via a neural network by learning the plurality of facial scansand using muscle or skin parameters derived from the plurality of facialscans as ground truth labels.
 2. The method of claim 1, whereindetermining, from the plurality of facial scans, the three-dimensionalfacial skin topology of the live actor comprises: determining, for asampled point on the three-dimensional facial skin topology, acorresponding point on the skull surface based on a respective tissuedepth corresponding to the sampled point; determining the skull surfacetopology by interpolating a plurality of determined corresponding pointson the skull surface.
 3. (canceled)
 4. The method of claim 3, furthercomprising: generating a muscle model from the plurality of the facialscans of the live actor, wherein the muscle model comprises a pluralityof facial muscles and each facial muscle includes an attachment point tothe skull; and refining the skull surface topology according to a set ofattachment points indicated by the generated muscle model.
 5. The methodof claim 1, further comprising: receiving, via a user interface, a userinput to modify the skull surface topology; and modifying the skullsurface topology according to the user input.
 6. The method of claim 1,wherein the tissue depth dataset is selected from a plurality of tissuedepth datasets based on a known characteristic of the live actor.
 7. Themethod of claim 6, wherein each dataset of the plurality of tissue depthdatasets corresponds to any combination of following factors: race; age;gender; height; weight; and body mass index.
 8. A system for generatinga skull surface of a live actor, the system comprising: an inputinterface configured to obtain a plurality of facial scans of the liveactor, each facial scan including a respective skin surface andrespective sensing data indicative of facial muscle strainscorresponding to the respective skin surface; a data interfaceconfigured to obtain, from a database, a tissue depth dataset includinga plurality of tissues depths corresponding to a plurality of tissuedepth points on a human face, wherein each tissue depth indicates adistance from a corresponding tissue depth point on the human face to askull surface underneath the human face; one or more processorsconfigured to: determine, from the plurality of facial scans, athree-dimensional facial skin topology of the live actor; generate, fromthe three-dimensional facial skin topology and the tissue depth dataset,a skull surface topology; and refine the skull surface topology via aneural network by learning the plurality of facial scans and usingmuscle or skin parameters derived from the plurality of facial scans asground truth labels.
 9. The system of claim 8, wherein the one or moreprocessors are further configured to determine, from the plurality offacial scans, the three-dimensional facial skin topology of the liveactor by: determining, for a sampled point on the three-dimensionalfacial skin topology, a corresponding point on the skull surface basedon a respective tissue depth corresponding to the sampled point;determining the skull surface topology by interpolating a plurality ofdetermined corresponding points on the skull surface.
 10. (canceled) 11.The system of claim 10, wherein the one or more processors are furtherconfigured to: generate a muscle model from the plurality of the facialscans of the live actor, wherein the muscle model comprises a pluralityof facial muscles and each facial muscle includes an attachment point tothe skull; and refine the skull surface topology according to a set ofattachment points indicated by the generated muscle model.
 12. Thesystem of claim 8, wherein the one or more processors are furtherconfigured to: receive, via a user interface, a user input to modify theskull surface topology; and modify the skull surface topology accordingto the user input.
 13. The system of claim 9, wherein the tissue depthdataset is selected from a plurality of tissue depth datasets based on aknown characteristic of the live actor.
 14. The system of claim 13,wherein each dataset of the plurality of tissue depth datasetscorresponds to any combination of following factors: race; age; gender;height; weight; and body mass index.
 15. A computer-readablenon-transitory medium storing computer-executable instructions forgenerating a skull surface of a live actor, the computer-executableinstructions being executed by one or more processors to performoperations comprising: obtaining a plurality of facial scans of the liveactor, each facial scan including a respective skin surface andrespective sensing data indicative of facial muscle strainscorresponding to the respective skin surface; obtaining, from adatabase, a tissue depth dataset including a plurality of tissues depthscorresponding to a plurality of tissue depth points on a human face,wherein each tissue depth indicates a distance from a correspondingtissue depth point on the human face to a skull surface underneath thehuman face; determining, from the plurality of facial scans, athree-dimensional facial skin topology of the live actor; generating,from the three-dimensional facial skin topology and the tissue depthdataset, a skull surface topology; and refining the skull surfacetopology via a neural network by learning the plurality of facial scansand using muscle or skin parameters derived from the plurality of facialscans as ground truth labels.
 16. The computer-readable non-transitorymedium of claim 15, wherein the operation of determining, from theplurality of facial scans, the three-dimensional facial skin topology ofthe live actor comprises: determining, for a sampled point on thethree-dimensional facial skin topology, a corresponding point on theskull surface based on a respective tissue depth corresponding to thesampled point; determining the skull surface topology by interpolating aplurality of determined corresponding points on the skull surface. 17.(canceled)
 18. The computer-readable non-transitory medium of claim 17,wherein the operations further comprise: generating a muscle model fromthe plurality of the facial scans of the live actor, wherein the musclemodel comprises a plurality of facial muscles and each facial muscleincludes an attachment point to the skull; and refining the skullsurface topology according to a set of attachment points indicated bythe generated muscle model.
 19. The computer-readable non-transitorymedium of claim 15, wherein the operations further comprise: receiving,via a user interface, a user input to modify the skull surface topology;and modifying the skull surface topology according to the user input.20. The computer-readable non-transitory medium of claim 15, wherein thetissue depth dataset is selected from a plurality of tissue depthdatasets based on a known characteristic of the live actor.
 21. Thecomputer-readable non-transitory medium of claim 20, wherein eachdataset of the plurality of tissue depth datasets corresponds to anycombination of following factors: race; age; gender; height; weight; andbody mass index.